Centralized configuration and monitoring of a distributed computing cluster

ABSTRACT

Systems and methods for centralized configuration and monitoring of a distributed computing cluster are disclosed. One embodiment of the disclose technology enables deployment and central operation a complete Hadoop stack. The application automates the installation process and reduces deployment time from weeks to minutes. One embodiment further provides a cluster-wide, real time view of the services running and the status of the host machines in a cluster via a single, central place to enact configuration changes across the computing cluster which further incorporates reporting and diagnostic tools to optimize cluster performance and utilization.

CROSS-REFERENCES TO RELATED APPLICATIONS

This application claims the benefit of, and incorporates by reference,U.S. Provisional Application No. 61/596,172 filed Feb. 7, 2012, andentitled “MANAGING THE SYSTEM LIFECYCLE AND CONFIGURATION OF APACHEHADOOP AND OTHER DISTRIBUTED SYSTEMS,” (Attorney Docket No.68784-8011.US00), U.S. Provisional Application No. 61/643,035, filed May4, 2012, and entitled “MANAGING THE SYSTEM LIFECYCLE AND CONFIGURATIONOF APACHE HADOOP AND OTHER DISTRIBUTED SYSTEMS” (Attorney Docket No.68784-8011.US01) and U.S. Provisional Application No. 61/642,937, filedMay 4, 2012, and entitled “CONFIGURING HADOOP SECURITY WITH CLOUDERAMANAGER,” (Attorney Docket No. 68784-8014.US00).

BACKGROUND

As powerful and useful as Apache Hadoop is, anyone who has setup up acluster from scratch is well aware of how challenging it can be: everymachine has to have the right packages installed and correctlyconfigured so that they can all work together, and if something goeswrong in that process, it can be even harder to nail down the problem.This is and has been be a serious barrier to adoption of Hadoop asdeployment and ongoing administration of a Hadoop stack can be difficultand time consuming.

In addition, deciding which components and versions to deploy based onuse cases; assigning roles for nodes; effectively configuring, startingand managing services across the cluster; and performing diagnostics tooptimize cluster performance requires significant expertise in modifyingservice installations and continuously ensuring that all the machines ina cluster are correctly and consistently configured

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates a system level block diagram of a computing clusterthat centrally configured and monitored by an end user through a userdevice over a network.

FIG. 2 depicts an architectural view of a system having a host serverand an agent for centralized configuration and monitoring of adistributed computing cluster.

FIG. 3 depicts an example of a distributed computing cluster that isconfigured and monitored by a host server in a centralized fashion andagents distributed among the hosts in the cluster.

FIG. 4 depicts another example of a distributed computing cluster thatis configured and monitored by a host server in a centralized fashionand agents distributed among the hosts in the cluster.

FIG. 5 depicts a flowchart of an example process for centralizedconfiguration of a distributed computing cluster.

FIG. 6 graphically depicts a list of configuration, management,monitoring, and troubleshooting functions of a computing clusterprovided via console or administrative console.

FIG. 7 depicts a flowchart of an example process of a server to utilizeagents to configure and monitor host machines in a computing cluster.

FIG. 8 depicts a flowchart showing example functions performed by agentsat host machines in a computing cluster for service configuration and toenable a host to compute health and performance metrics.

FIG. 9 depicts a flowchart of an example process for centralizedconfiguration, health, performance monitoring, and event alerting of adistributed computing cluster.

FIG. 10-11 depict example screenshots showing the installation processwhere hosts are selected and added for the computing cluster setup andinstallation.

FIG. 12 depicts an example screenshot for inspecting host details forhosts in a computing cluster.

FIG. 13 depicts an example screenshot for monitoring the services thatare running in a computing cluster.

FIG. 14-15 depict example screenshots showing the configuration processof hosts in a computing cluster including selection of services,selecting host assignments/roles to the services, and showing serviceconfiguration recommendations.

FIG. 16A-B depicts example screenshots showing user environments forreviewing configuration changes.

FIG. 17 depicts example actions that can be performed on the services.

FIG. 18 depicts example screenshot showing a user environment forviewing actions that can be performed on role instances in the computingcluster.

FIG. 19 depicts an example screenshot showing user environment forconfiguration management.

FIG. 20 depicts an example screenshot showing user environment forsearching among configuration settings.

FIG. 21 depicts an example screenshot showing user environment forannotating configuration changes or settings.

FIG. 22 depicts an example screenshot showing user environment forviewing the configuration history for a service.

FIG. 23 depicts an example screenshot showing user environment forconfiguration review and rollback.

FIG. 24 depicts an example screenshot showing user environment formanaging the users and managing the associated permissions.

FIG. 25 depicts an example screenshot showing user environment foraccessing an audit history of a computing cluster and its services.

FIG. 26-27 depicts example screenshots showing user environment forviewing system status, usage statistics, and health information.

FIG. 28 depicts an example screenshot showing user environment foraccessing or searching log files.

FIG. 29 depicts an example screenshot showing user environment formonitoring activities in the computing cluster.

FIG. 30 depicts an example screenshot showing user environment showingtask distribution.

FIG. 31 depicts an example screenshot showing user environment showingreports of resource consumption and usage statistics in the computingcluster.

FIG. 32 depicts an example screenshot showing user environment forviewing health and performance data of an HDFS service.

FIG. 33 depicts an example screenshot showing user environment forviewing a snapshot of system status at the host machine level of thecomputing cluster.

FIG. 34 depicts an example screenshot showing user environment forviewing and diagnosing cluster workloads.

FIG. 35 depicts an example screenshot showing user environment forgathering, viewing, and searching logs.

FIG. 36 depicts an example screenshot showing user environment fortracking and viewing events across a computing cluster.

FIG. 37 depicts an example screenshot showing user environment forrunning and viewing reports on system performance and usage.

FIG. 38-39 depicts example screenshots showing time interval selectorsfor selecting a time frame within which to view service information.

FIG. 40 depicts a table showing examples of different levels of healthmetrics and statuses.

FIG. 41 depicts another screenshot showing the user environment formonitoring health and status information for MapReduce service runningon a computing cluster.

FIG. 42 depicts a table showing examples of different service or roleconfiguration statuses.

FIG. 43-44 depicts example screenshots showing the user environment foraccessing a history of commands issued for a service (e.g., HUE) in thecomputing cluster.

FIG. 45-49 depict example screenshots showing example user interfacesfor managing configuration changes and viewing configuration history.

FIG. 50A depicts an example screenshot showing the user environment forviewing jobs and running job comparisons with similar jobs.

FIG. 50B depicts a table showing the functions provided via the userenvironment of FIG. 50A.

FIG. 50C-D depict example legends for types of jobs and different jobstatuses shown in the user environment of FIG. 50A.

FIG. 51-52 depict example screenshots depicting user interfaces whichshow resource and service usage by user.

FIG. 53-57 depict example screenshots showing user interfaces formanaging user accounts.

FIG. 58-59 depict example screenshots showing user interfaces forviewing applications recently accessed by users.

FIG. 60-62 depict example screenshots showing user interfaces formanaging user groups.

FIG. 63-64 depict example screenshots showing user interfaces formanaging permissions for applications by service or by user groups.

FIG. 65 depicts an example screenshot showing the user environment forviewing recent access information for users.

FIG. 66-68 depicts example screenshots showing user interfaces forimporting users and groups from an LDAP directory.

FIG. 69 depicts an example screenshot showing the user environment formanaging imported user groups.

FIG. 70 depicts an example screenshot showing the user environment forviewing LDAP status.

FIG. 71 shows a diagrammatic representation of a machine in the exampleform of a computer system within which a set of instructions, forcausing the machine to perform any one or more of the methodologiesdiscussed herein, may be executed.

DETAILED DESCRIPTION

The following description and drawings are illustrative and are not tobe construed as limiting. Numerous specific details are described toprovide a thorough understanding of the disclosure. However, in certaininstances, well-known or conventional details are not described in orderto avoid obscuring the description. References to one or an embodimentin the present disclosure can be, but not necessarily are, references tothe same embodiment; and, such references mean at least one of theembodiments.

Reference in this specification to “one embodiment” or “an embodiment”means that a particular feature, structure, or characteristic describedin connection with the embodiment is included in at least one embodimentof the disclosure. The appearances of the phrase “in one embodiment” invarious places in the specification are not necessarily all referring tothe same embodiment, nor are separate or alternative embodimentsmutually exclusive of other embodiments. Moreover, various features aredescribed which may be exhibited by some embodiments and not by others.Similarly, various requirements are described which may be requirementsfor some embodiments but not other embodiments.

The terms used in this specification generally have their ordinarymeanings in the art, within the context of the disclosure, and in thespecific context where each term is used. Certain terms that are used todescribe the disclosure are discussed below, or elsewhere in thespecification, to provide additional guidance to the practitionerregarding the description of the disclosure. For convenience, certainterms may be highlighted, for example using italics and/or quotationmarks. The use of highlighting has no influence on the scope and meaningof a term; the scope and meaning of a term is the same, in the samecontext, whether or not it is highlighted. It will be appreciated thatsame thing can be said in more than one way.

Consequently, alternative language and synonyms may be used for any oneor more of the terms discussed herein, nor is any special significanceto be placed upon whether or not a term is elaborated or discussedherein. Synonyms for certain terms are provided. A recital of one ormore synonyms does not exclude the use of other synonyms. The use ofexamples anywhere in this specification including examples of any termsdiscussed herein is illustrative only, and is not intended to furtherlimit the scope and meaning of the disclosure or of any exemplified termLikewise, the disclosure is not limited to various embodiments given inthis specification.

Without intent to further limit the scope of the disclosure, examples ofinstruments, apparatus, methods and their related results according tothe embodiments of the present disclosure are given below. Note thattitles or subtitles may be used in the examples for convenience of areader, which in no way should limit the scope of the disclosure. Unlessotherwise defined, all technical and scientific terms used herein havethe same meaning as commonly understood by one of ordinary skill in theart to which this disclosure pertains. In the case of conflict, thepresent document, including definitions will control.

Embodiments of the present disclosure include systems and methods forcentralized configuration, monitoring, troubleshooting, and/ordiagnosing a distributed computing cluster.

FIG. 1 illustrates a system level block diagram of a computing cluster108 that centrally configured and monitored by an end user through aclient device 102 (e.g., via a web browser 150) over a network 106.

The client device 102 can be any system and/or device, and/or anycombination of devices/systems that is able to establish a connectionwith another device, a server and/or other systems. The client device102 typically includes a display or other output functionalities topresent data exchanged between the devices to a user, for examplethrough a user interface 104. The user interface 104 can be used toaccess a web page via browser 150 used to access an application orconsole enabling configuration and monitoring of the distributedcomputing cluster 108.

The console accessed via the browser 150 is coupled to the server 100via network 106 which is able to manage the configuration settings,monitor the health of the services running in the cluster 108, andmonitor or track user activity on the cluster 108. In one embodiment,the console or user environment accessed via browser 150 to control theserver 100 which provides an end-to-end management application for frameworks supporting distributed applications that run on a distributedcomputing cluster 108 such as Apache Hadoop and other related services.The server 100 is able to provide granular visibility into and controlover the every part of the cluster 108, and enables operators or usersto improve cluster performance, enhance quality of service, increasecompliance and reduce administrative costs.

The console or user environment provided by the server 100 can allowdistributed application frameworks (e.g., Hadoop services) to be easilydeploy and centrally operated. The application can automate theinstallation process, and reduce deployment time from weeks to minutes.In addition, through the console, the server 100 provides a cluster-wideand real time or near real time view of the services running and thestatus of their hosts. In addition, the server 100, through the consoleor user environment accessed via a web browser 150 can provide a single,central place to enact configuration changes across the computingcluster and incorporate reporting and diagnostic tools to assist withcluster performance optimization and utilization. Some example functionsperformed by the server 100 include, for example:

Installs the complete Hadoop stack or other distributed applicationmanagement frame work in minutes via a wizard-based interface.

Provides end-to-end visibility and control over the computing clusterfrom a single interface.

Correlates jobs, activities, logs, system changes, configuration changesand service metrics along a timeline to simplify diagnosis.

Allows users to set server roles, configure services and manage securityacross the cluster.

Allows users to gracefully start, stop and restart of services asneeded.

Maintains a complete record of configuration changes with the ability toroll back to previous states.

Monitors dozens of service performance metrics and generates alerts whencritical thresholds are approached, reached or exceeded.

Allows users to gather, view and search logs collected from across thecluster.

Creates and aggregates relevant events pertaining to system health, logmessages, user services and activities and makes them available foralerting (by email) and searching.

Consolidates cluster activity (user jobs) into a single, real-time view.

Allows users to drill down into individual workflows and jobs at thetask attempt level to diagnose performance issues.

Shows information pertaining to hosts in the cluster including status,resident memory, virtual memory and roles.

Provides operational reports on current and historical disk usage byuser, group, and directory, as well as service activity (e.g., MapReduceactivity) on the cluster by job or user.

Takes a snapshot of the cluster state and automatically sends it tosupport to assist with problem resolution.

The client device 102 can be, but are not limited to, a server desktop,a desktop computer, a thin-client device, an internet kiosk, a computercluster, a mobile computing device such as a notebook, a laptopcomputer, a handheld computer, a mobile phone, a smart phone, a PDA, aBlackberry device, a Treo, and/or an iPhone, etc. In one embodiment, theclient device 102 is coupled to a network 106.

In one embodiment, users or developers interact with the client device102 (e.g., machines or devices) to access the server 100 and servicesprovided therein. Specifically, users, enterprise operators, systemadmins, or software developers can configure, access, monitor, orreconfigure the computing cluster 108 by interacting with the server 100via the client device 102. The functionalities and features of userenvironment which enables centralized configuration and/or monitoringare illustrated with further references to the example screenshots ofFIG. 10-FIG. 70.

In operation, end users interact with the computing cluster 108 (e.g.,machines or devices). As a results of the user interaction, the cluster108 can generate datasets such as log files to be collected andaggregated. The file can include logs, information, and other metadataabout clicks, feeds, status updates, data from applications, andassociated properties and attributes. The computer cluster 108 can bemanaged under the Hadoop framework (e.g., via the Hadoop distributedfile system or other file systems which may be distributed file systems,non-distributed file systems, distributed fault-tolerant file systems,parallel file systems, peer-to-peer file systems, including but notlimited to, CFS, Unilium, OASIS, WebDFS, CloudStore, Cosmos, dCache,Parallel Virtual File System, Starfish, DFS, NFS, VMFS, OCFS, CXFS,DataPlow SAN File System, etc.). Such log files and analytics can beaccessed or manipulated through applications hosted by the server 100(e.g., supported by the Hadoop framework, Hadoop services, or otherservices supporting distributed applications and clusters).

The network 106, over which the client device 102, server 100, andcluster 208 communicate may be a telephonic network, an open network,such as the Internet, or a private network, such as an intranet and/orthe extranet. For example, the Internet can provide file transfer,remote log in, email, news, RSS, and other services through any known orconvenient protocol, such as, but is not limited to the TCP/IP protocol,Open System Interconnections (OSI), FTP, UPnP, iSCSI, NSF, ISDN, PDH,RS-232, SDH, SONET, etc.

The network 106 can be any collection of distinct networks operatingwholly or partially in conjunction to provide connectivity to the clientdevices, host server, and may appear as one or more networks to theserviced systems and devices. In one embodiment, communications to andfrom the client device 102 can be achieved by, an open network, such asthe Internet, or a private network, such as an intranet and/or theextranet. In one embodiment, communications can be achieved by a securecommunications protocol, such as secure sockets layer (SSL), ortransport layer security (TLS).

The term “Internet” as used herein refers to a network of networks thatuses certain protocols, such as the TCP/IP protocol, and possibly otherprotocols such as the hypertext transfer protocol (HTTP) for hypertextmarkup language (HTML) documents that make up the World Wide Web (theweb). Content is often provided by content servers, which are referredto as being “on” the Internet. A web server, which is one type ofcontent server, is typically at least one computer system which operatesas a server computer system and is configured to operate with theprotocols of the World Wide Web and is coupled to the Internet. Thephysical connections of the Internet and the protocols and communicationprocedures of the Internet and the web are well known to those of skillin the relevant art. For illustrative purposes, it is assumed thenetwork 106 broadly includes anything from a minimalist coupling of thecomponents illustrated in the example of FIG. 1, to every component ofthe Internet and networks coupled to the Internet.

In addition, communications can be achieved via one or more wirelessnetworks, such as, but is not limited to, one or more of a Local AreaNetwork (LAN), Wireless Local Area Network (WLAN), a Personal areanetwork (PAN), a Campus area network (CAN), a Metropolitan area network(MAN), a Wide area network (WAN), a Wireless wide area network (WWAN),Global System for Mobile Communications (GSM), Personal CommunicationsService (PCS), Digital Advanced Mobile Phone Service (D-Amps),Bluetooth, Wi-Fi, Fixed Wireless Data, 2G, 2.5G, 3G, 4G, LTE networks,enhanced data rates for GSM evolution (EDGE), General packet radioservice (GPRS), enhanced GPRS, messaging protocols such as, TCP/IP, SMS,MMS, extensible messaging and presence protocol (XMPP), real timemessaging protocol (RTMP), instant messaging and presence protocol(IMPP), instant messaging, USSD, IRC, or any other wireless datanetworks or messaging protocols.

The client device 102 can be coupled to the network (e.g., Internet) viaa dial up connection, a digital subscriber loop (DSL, ADSL), cablemodem, and/or other types of connection. Thus, the client device 102 cancommunicate with remote servers (e.g., web server, host server, mailserver, and instant messaging server) that provide access to userinterfaces of the World Wide Web via a web browser, for example.

The repository 130, though illustrated to be coupled to the server 100,can also be coupled to the computing cluster 108, either directly or vianetwork 106. In one embodiment, the repository 130 can store catalog ofthe available host machines in the cluster 108, and the services, roles,and configurations assigned to each host.

The repository 130 can additionally store software, descriptive data,images, system information, drivers, collected datasets, aggregateddatasets, log files, analytics of collected datasets, enriched datasets,etc. The repository may be managed by a database management system(DBMS), for example but not limited to, Oracle, DB2, Microsoft Access,Microsoft SQL Server, MySQL, FileMaker, etc.

The repository can be implemented via object-oriented technology and/orvia text files, and can be managed by a distributed database managementsystem, an object-oriented database management system (OODBMS) (e.g.,ConceptBase, FastDB Main Memory Database Management System,JDOInstruments, ObjectDB, etc.), an object-relational databasemanagement system (ORDBMS) (e.g., Informix, OpenLink Virtuoso, VMDS,etc.), a file system, and/or any other convenient or known databasemanagement package.

FIG. 2 depicts an architectural view of a system having a host server200 and an agent 250 for centralized configuration and monitoring of adistributed computing cluster.

The system includes the host server 200 components and the agent 250components on each host machine 248 which is part of a computing cluster(e.g., as shown in the examples of FIG. 3 and FIG. 4). The host server200 can track the data models (e.g., by the data model tracking engine204), which can be stored in the database 230. The data model caninclude a catalog of the available host machines in the cluster, and theservices, roles, and configurations that are assigned to each host.

In addition, the host server 200 performs the following functions:communicates with agents (e.g., by the communication module 214) to sendconfiguration instructions and track agents' 250 heartbeats (e.g., bythe agent tracking engine 216), performs command execution (e.g., by thecommand execution engine 208) to perform tasks in the cluster, providesa console for the operator (e.g., by the web server and admin consolemodule 206) to perform management and configuration tasks.

In addition, the host server 200 creates, reads, validates, updates, anddeletes configuration settings or generates recommended configurationsettings based on resources available to each machine in the cluster.For example, through the console or user environment, the user oroperator can view the suggested ranges of values for parameters and viewthe illegal values for the parameters. In addition, override settingscan also be configured on specific hosts through the user environment.

The host server 200 further calculates and displays health of cluster(e.g., by the cluster health calculation engine 212), tracks disk usage,CPU, and RAM, manages monitors the health of daemons (e.g., Hadoopdaemons), generates service performance metrics, generates/deliversalerts when critical thresholds are detected. In addition, the hostserver 200 can generate and maintain a history of activity monitoringdata and configuration changes

Agents 250 can be deployed to each machine in a cluster. The agents areconfigured by the host server 200 with settings and configurationsettings for services and roles assigned to each host machine in thecluster. Each agent 250 starts and stops Hadoop daemons (e.g., by theservice installation engine 252) on the host machine and collectsstatistics (overall and per-process memory usage and CPU usage, logtailing) for health calculations and status (e.g., by the performanceand health statistics collector 254) in the console. In one embodiment,the agent 250 runs as root on a host machine in a cluster to ensure thatthe required directories are created and that processes and files areowned by or associated with the appropriate user (for example, the HDFSuser and MapReduce user) since multiple users can access any givencluster and start any service (e.g., Hadoop services).

FIG. 3 depicts an example of a distributed computing cluster 308 that isconfigured and monitored by a host server 300 using agents 350distributed among the host machines 348 in the computing cluster 308.

To use the console for centralized configuration and monitoring of thecluster 308, a database application can be installed on the host server300 or on one of the machines 348 in the cluster 308 that the server 300can access. In addition, Hadoop or other distributed applicationframeworks and the agents 350 are installed on the other host machines348 in the cluster 308.

FIG. 4 depicts an example of how the console/user environment can beused to configure the host machines 448 in the computing cluster 408 forthe various instances of services and roles.

During installation, the first run of a wizard is used to add andconfigure the services (e.g., Hadoop services) to be run on the hosts448 in the cluster 408. After the first run of the wizard, the consolecan be used and accessed to reconfigure the existing services, and/or toadd and configure more hosts and services. In general, when a servicesis added or configured, an instance of that service is running in thecluster 408 and that the services can be uniquely configured and thatmultiple instances of the services can be run in the cluster 408.

After a service has been configured, each host machine 448 in thecluster 408 can then be configured with one or more functions (e.g., a“role”) for it to perform under that service. The role typicallydetermines which daemons (e.g., Hadoop daemons) are run on which hostmachines 448 in the cluster 408, which is what defines the role the hostmachine performs in the Hadoop cluster. For example, after an HDFSservice instance called hdfs1 is configured, one host machine 448 a canbe configured or selected to run as a NameNode, another host 448 b torun as a Secondary NameNode, another host to run as a Balancer, and theremaining hosts as DataNodes (e.g., 448 d and 448 e).

This configuration process adds role instances by selecting or assigninginstances of each type of role (NameNode, DataNode, and so on) to hostsmachines 448 in the cluster 408. In this example, these roles instancesrun under the hdfs1 service instance. In another example, a Map/Reduceservice instance called mapreduce1 can be configured. To run undermapreduce1, one host 448 c to run as a JobTracker role instance, otherhosts (e.g., 448 d and 448 e) to run as TaskTracker role instances.

As shown in the example of FIG. 4, hdfs1 is the name of an HDFS serviceinstance. The associated role instances in this example are calledNAMENODE-1, SECONDARYNAMENODE-1, DATANODE-1, and DATANODE-<n>, which rununder the hdfs1 service instance on those same hosts. Note that althoughthe illustration only shows two DataNode hosts, the cluster 408 caninclude any number of DataNode hosts. Similarly, mapreduce1, zookeeper1,and hbase1 are examples of service instances that have associated roleinstances running on hosts 448 in the cluster 408 (for example,JOBTRACKER-1, zookeeper-1-SERVER-1, and hbase1-MASTER-1).

Furthermore, additional tasks to manage, configure and supervise daemons(e.g., Hadoop daemons) on host machines 448 can be performed. Forexample, the first time the console is used or started, a wizard can belaunched to install a distributed application management framework(e.g., any Hadoop distribution) and JDK on the host machines 448 and toconfigure and start services.

In general, after the first run, the console/user environment canfurther used to configure the distributed application frame work (e.g.,Hadoop) using or referencing suggested ranges of values for parametersand identified illegal values, start and stop Hadoop daemons on the hostmachines 448, monitor the health of the commuting cluster 408, view thedaemons that are currently running, add and reconfigure services androle instances.

The console can further, for example, display metrics about jobs, suchas the number of currently running tasks and their CPU and memory usage,display metrics about the services (e.g., Hadoop services) such as theaverage HDFS I/O latency and the number of jobs running concurrently,display metrics about the cluster 408, such as the average CPU loadacross all machines 448.

In one embodiment, the console can be used to specify dependenciesbetween services such that configuration changes for a service can bepropagated to its dependent service. In one embodiment, the host server400 can automatically detect or determine dependences between differentservices that are run in the cluster 408.

Furthermore, configuration settings can be imported and exported to andfrom clusters 408 by the host server 400 can controlled via the consoleat device 402. The server can also generate configurations (e.g., Hadoopconfigurations) for clients to use to connect to the cluster 408, and/ormanage rack locality configuration. For example, to allow Hadoop clientusers to work with the HDFS, MapReduce, and HBase services, a zip filethat contains the relevant configuration files with the settings forservices can be generated and distributed to other users of a givenservice. In one embodiment, the host server 400 is able to collapseseveral levels of Hadoop configuration abstraction into one. Forexample, Java heap usage can be managed in the same place asHadoop-specific parameters.

Note that one of the aspects of Hadoop configuration is what machinesare physically located on what rack. This is an approximation fornetwork bandwidth: there is more network bandwidth within a rack thanacross racks. It is also an approximation for failure zones, forexample, if there is one switch per rack, and if that switch has afailure, then the entire rack is out. Hadoop places files in such a waythat a switch failure can typically be tolerated. Rack localityconfiguration services tells which hosts are in what racks and allowsthe system to tolerate single switch failures.

FIG. 5 depicts a flowchart of an example process for centralizedconfiguration of a distributed computing cluster.

In process 502, a user environment enabling a selection of a service tobe run on hosts in the distributed computing cluster is provided. In oneembodiment, the user environment is accessed via a web browser on anyuser device by a user, system admin, or other operator, for example. Theservice includes one or more Hadoop services including by way of examplebut not limitation, Hbase, Hue, ZooKeeper and Oozie, Hadoop Common,Avro, Cassandra, Chukwa, Hive, Mahout, and Pig.

In process 504, recommended configuration settings of the service or thehosts in the distributed computing cluster to run the service aregenerated. In process 506, the recommended configuration settings of theservice are provided via the user environment. The recommendedconfiguration settings can include, for example, suggested ranges forparameters and invalid values for the parameters.

In one embodiment, the user environment further enables configuration ofthe service or hosts in the distributed computer cluster. Additionalfeatures/functions provided via the user environment are furtherillustrated at Flow ‘A’ in FIG. 6. In process 508, a user accesses theuser environment to select the configuration and/or to access therecommended configuration settings. In process 510, agents are deployedto the hosts in the distributed computing cluster to configure each ofthe hosts. In process 512, each of the hosts in the distributedcomputing cluster is configured to run the service based on a set ofconfiguration settings.

FIG. 6 graphically depicts a list of configuration, management,monitoring, and troubleshooting functions of a computing clusterprovided via console or administrative console depicted in userenvironment 602.

The console enables actions to be performed on the set of configurationsettings 604, the actions can include, for example, one or more of,reading, validating, updating, and deleting the configuration settings.Such actions can typically be performed at any time before installation,during installation, during maintenance/downtown, during runtime/operation of the services or Hadoop-based services in a computingcluster. The Hadoop services include one or more of, MapReduce, HDFS,Hue, ZooKeeper and Oozie.

The console enables addition of services and reconfiguration of theservice 606, including selection of services during installation orsubsequent reconfiguration. The console enables assignment andre-assignment of roles to each of the hosts 608, as illustrated in theexample screenshots of FIG. 14-FIG. 15. The console enables userconfiguration of the hosts with functions to perform under the service610, as illustrated in the example screenshots of FIG. 16A-16B.

The console enables the selection of the service during an installationphase under the service 612, as illustrated in the example screenshotsof FIG. 14. The console displays current or historical health status ofthe hosts 614, and can further indicate, one or more of, current orhistorical performance metrics of the service, a history of actionsperformed on the service, or a log of configuration changes of theservice.

In one embodiment, the user environment further displays performancemetrics of a job or comparison or performance of similar jobs, asillustrated in the example screenshot of FIG. 50A. The console displayscurrent or historical disk usage, CPU, virtual memory consumption, orRAM usage of the hosts 616, as illustrated in the example screenshot ofFIG. 31.

The console displays operational reports 618. The operational reportscan include one or more of, disk use by user, user group, or directory,cluster job activity by user, group or job ID. The console indicatescurrent or historical performance metrics or operational status of thehosts 620, as illustrated in the example screenshot of FIG. 37. Theconsole can also indicate current user activities or historical useractivities on the distributed computing cluster 622, and can furtherdisplay a history of activity monitoring data and configuration changesof the hosts in the distributed computing cluster.

The console provides access to log entries associated with the serviceor events 624, as illustrated in the example screenshot of FIG. 28.Events can include any record that something of interest has occurred—aservice's health has changed state, a log message (of the appropriateseverity) has been logged, and so on. The system can aggregates Hadoopevents and makes them available for alerting and for searching.

Thus, a history of all relevant events that occur cluster-wide can begenerated and provided. The events can include, for example, a record ofchange of state of health of the server, a message has been logged, aservice has been added or reconfigured, a new job has been setup, anerror, a change in operational or on/off state of a given host. Theevents can further, one or more of, a health check event, a log messageevent, an audit event, and an activity event.

Health check events can include, occurrence of certain health checkactivities, or that health check results have met specific conditions(thresholds). Log message events can include events generated forcertain types of log messages from HDFS, MapReduce, or HBase servicesand roles. Log events are created when a log entry matches a set ofrules for messages of interest. In general, audit events are generatedby actions taken by the management system, such as creating, deleting,starting, or stopping services or roles. Activity events can includeevents generated for jobs that fail, or that run slowly (as determinedby comparison with duration limits)

In one embodiment, the events are searchable via the user environment.The user environment further enables search or filtering of the logentries by one or more of, time range, service, host, keyword, and user.

The user environment can further depict alerts triggered by certainevents or actions in the distributed computing cluster. In oneembodiment, the user environment further enables configuration ofdelivery of alerts. For any given service or role instance, summarylevel alerts and/or individual health check alerts can be enabled ordisabled. Summary alerts can be sent when the overall health for a roleor service becomes unhealthy. Individual alerts occur when individualhealth checks for the role or service fail or become critical. Forexample, service instances of type HDFS, MapReduce, and HBase cangenerate alerts if so configured

FIG. 7 depicts a flowchart of an example process of a server to utilizeagents to configure and monitor host machines in a computing cluster.

In process 702, a data model with a catalog of hosts in the computingcluster is tracked and updated. In one embodiment, the data model isstored in a repository coupled to the server. The data model canspecify, one or more of, services, roles, and configurations assigned toeach of the hosts. The data model can further store configuration ormonitoring information regarding the daemons on each of the hosts.

In process 704, a console for management and configuration of servicesto be deployed in the computing cluster is provided. In process 706,agents to be deployed to the hosts in the computing cluster areconfigured based on configuration settings.

In process 708, the agents are deployed to each of the hosts andcommunicate with the agents to send the configuration settings toconfigure each of the hosts in the computing cluster. The processesperformed by the agents are further illustrated in the example flowchart of FIG. 8.

In process 710, health and performance metrics of the hosts and theservices are monitored and agent heartbeats are tracked. In process 712,the health and the performance metrics of the hosts and the services aredepicted in the console. In process 714, a history of the health and theperformance metrics is maintained. In process 716, health calculationsof the hosts are performed based on the statistics collected by theagents.

FIG. 8 depicts a flowchart showing example functions performed by agentsat host machines in a computing cluster for service configuration and toenable a host to compute health and performance metrics.

In process 802, agents start daemons on each of the hosts to run theservices. In process 804, directories, processes, and files are createdon hosts in a user-specific manner. In process 806, the agents aggregatestatistics regarding each of the hosts. In process 808, the agentscommunicate and send heartbeats to the server.

Agent heartbeat interval and timeouts to trigger changes in agent healthstatus can be configured. For example, The interval between eachheartbeat that is sent from agents to the host server can be set. If anagent fails to send this number of heartbeats fail x number ofconsecutive heartbeats to the Server, a concerning health status isassigned to that agent. Similarly, if an Agent fails to send a certainnumber of expected consecutive heartbeats to the Server, a bad healthstatus can be assigned to that agent.

In process 810, the health and the performance metrics of the hosts andthe services are depicted in a console. In process 812, a history of thehealth and the performance metrics are maintained. In process 814,health calculations of the hosts are performed based on the statisticscollected by the agents.

FIG. 9 depicts a flowchart of an example process for centralizedconfiguration, health, performance monitoring, and event alerting of adistributed computing cluster.

In process 902, hosts in the computing cluster are configured based onconfiguration settings and Hadoop services to be run in the computingcluster. The configuration settings can be specified via a consoleaccessible via a web interface. In one embodiment, enablement ofselection of a service during installation to be run on hosts in thedistributed computing cluster is provided via the console. In addition,recommended configuration settings of the Hadoop service or the hosts inthe computing cluster to run the service can be provided via theconsole.

In process 904, health and performance metrics of the hosts and theHadoop services are monitored. In process 906, the health and theperformance metrics of the hosts and the Hadoop services are computed.In process 908, the health and the performance metrics of the hosts andthe Hadoop services are depicted in the console. In general, the healthand the performance metrics include current information regarding thecomputing cluster in real time or near real time. The health and theperformance metrics can also include historical information regardingthe computing cluster.

In process 910, an event in the computing cluster meeting a criterion orthreshold is detected. In process 912, an alert is generated. Alerts canbe delivered via any number of electronic means including, but notlimited to, email, SMS, instant messages, etc. The system can beconfigured to generate alerts from a variety of events. In addition,thresholds can be specified or configured for certain types of events,enabled/disabled, and configured for push delivery of on criticalevents.

FIG. 10-11 depict example screenshots showing the installation processwhere hosts are selected 1000 and added for the computing cluster setupand installation of packages 1100.

FIG. 12 depicts an example screenshot 1200 for inspecting host detailsfor hosts in a computing cluster. Host details including hostinformation 1202, processes 1206 and roles 1204 that can be shown. Theprocesses panel 1206 can show the processes that run as part of thisservice role, with a variety of metrics about those processes

FIG. 13 depicts an example screenshot 1300 for monitoring the servicesthat are running in a computing cluster.

FIG. 14-15 depict example screenshots showing the configuration processof hosts in a computing cluster including selection of services 1400,selecting host assignments/roles to the services 1500, and showingservice configuration recommendations 1500.

FIG. 16A-B depicts example screenshots 1600 and 1650 showing userenvironments for reviewing configuration changes.

FIG. 17 depicts user interface features 1700 showing example actionsthat can be performed on the services. The actions that can be performedinclude generic actions 1702 and service-specific actions 1704. Theactions menu can be accessed from the service status page. The commandsfunction at the Service level—for example, restart selected from thispage will restart all the roles within this service.

FIG. 18 depicts example screenshot showing a user environment 1800 forviewing actions 1802 that can be performed on role instances in thecomputing cluster.

The instances page shown in 1800 displays the results of theconfiguration validation checks it performs for all the role instancesfor this service. The information on this page can include: Each roleinstance by name, The host on which it is running, the rack assignment,the role instance's status and/or the role instance's health. Inaddition, the instances list can be sorted and filtered by criteria inany of the displayed columns.

FIG. 19 depicts an example screenshot showing user environment 1900 forconfiguration management.

Services configuration enables the management of the deployment andconfiguration of the computing cluster. The operator or user can add newservices and roles if needed, gracefully start, stop and restartservices or roles, and decommission and delete roles or services ifnecessary. Further, the user can modify the configuration properties forservices or for individual role instances, with an audit trail thatallows configuration roll back if necessary. Client configuration filescan also be generated. After initial installation, the ‘add a service’wizard can be used to add and configure new service instances. The newservice can be verified to have started property by navigating toServices>Status and checking the health status for the new service.After creating a service using one of the wizards, the user can add arole instance to that service. For example, after initial installationin which HDFS service was added, the user or operator can also specify aDataNode to a host machine in the cluster where one was not previouslyrunning

Similarly a role instance can be removed, for example, a role instancesuch as a DataNode can be removed from a cluster while it is running bydecommissioning the role instance. When a role instance isdecommissioned, system can perform a procedure to safely retire the nodeon a schedule to avoid data loss.

FIG. 20 depicts an example screenshot showing user environment 2000 forsearching among configuration settings in the search field 2002.

FIG. 21 depicts an example screenshot showing user environment 2100 forannotating configuration changes or settings in field 2102.

FIG. 22 depicts an example screenshot showing user environment 2200 forviewing the configuration history for a service.

FIG. 23 depicts an example screenshot showing user environment 2300 forconfiguration review and rollback.

Whenever a set of configuration settings are changed and saved for aservice or role instance, the system saves a revision of the previoussettings and the name of the user who made the changes. The pastrevisions of the configuration settings can be viewed, and, if desired,roll back the settings to a previous state. FIG. 24 depicts an examplescreenshot showing user environment 2400 for managing users and managingtheir permissions.

FIG. 25 depicts an example screenshot showing user environment 2500 foraccessing an audit history of a computing cluster and its services.

The user environment 2500 accessed via the audit table depicts theactions that have been taken for a service or role instance, and whatuser performed them. The audit history can include actions such ascreating a role or service, making configuration revisions for a role orservice, and running commands. In general, the audit history can includethe following information: Context: the service or role and hostaffected by the action, message: What action was taken, date: date andtime that the action was taken, user: the user name of the user thatperformed the action.

FIG. 26-27 depicts example screenshots showing user environment 2600 and2700 for viewing system status, usage statistics, and healthinformation. For example, current service status 2702, results of healthtests 2708, summary of daemon health status 2704, and/or graphs ofperformance with respect to time 2706 can be generated and displayed.

The services page opens and shows an overview of the service instancescurrently installed on the cluster. In one embodiment, for each serviceinstance, this can show, for example: The type of service; the servicestatus (for example, started); the overall health of the service; thetype and number of the roles that have been configured for that serviceinstance.

For all service types there is a Status and Health Summary that shows,for each configured role, the overall status and health of the roleinstance(s). In general, most service types can provide tabs at thebottom of the page to view event and log entries related to the serviceand role instances shown on the page. Note that HDFS, MapReduce, andHBase services also provide additional information including, forexample: a snapshot of service-specific metrics, health test results,and a set of charts that provide a historical view of metrics ofinterest. FIG. 28 depicts an example screenshot showing user environment2800 for accessing or searching log files.

FIG. 29 depicts an example screenshot showing user environment 2900 formonitoring activities in the computing cluster. For example, userenvironment 2900 can include search filters 2902, show the jobs that arerun in a given time period 2904, and/or cluster wide and/or per-jobgraphs 2906.

FIG. 30 depicts an example screenshot showing user environment 3000showing task distribution.

The task distribution chart of 3000 can create a map of the performanceof task attempts based on a number of different measures (on the Y-axis)and the length of time taken to complete the task on the X-axis. Thechart 3000 shows the distribution of tasks in cells that represent therelationship of task duration to values of the Y-axis metric. The numberin each cell shows the number of tasks whose performance statistics fallwithin the parameters of the cell.

The task distribution chart of 3000 is useful for detecting tasks thatare outliers in the jobs, either because of skew, or because of faultyTaskTrackers. The chart can show if some tasks deviate significantlyfrom the majority of task attempts. Normally, the distribution of taskswill be fairly concentrated. If, for example, some Reducers receive muchmore data than others, that will be represented by having two discretesections of density on the graph. That suggests that there may be aproblem with the user code, or that there's skew in the underlying data.Alternately, if the input sizes of various Map or Reduce tasks are thesame, but the time it takes to process them varies widely, it might meanthat certain TaskTrackers are performing more poorly than others.

In one embodiment, each cell is accessible to see a list of theTaskTrackers that correspond to the tasks whose performance falls withinthe cell. The Y-axis can show Input or Output records or bytes for Mapor Reduce tasks, or the amount of CPU seconds for the user who ran thejob, while the X-axis shows the task duration in seconds.

In addition, the distribution of the following can also be charted: MapInput Records vs. Duration, Map Output Records vs. Duration, Map InputBytes vs. Duration, Map Output Bytes vs. Duration, Current User CPUs(CPU seconds) vs. Duration, Reduce Input Records vs. Duration, ReduceOutput Records vs. Duration, Reduce Input Bytes vs. Duration, ReduceOutput Bytes vs. Duration, TaskTracker Nodes.

To the right of the chart is a table that shows the TaskTracker hoststhat processed the tasks in the selected cell, along with the number oftask attempts each host executed. Cells in the table can be selected toview the TaskTracker hosts that correspond to the tasks in the cell. Thearea above the TaskTracker table shows the type of task and range ofdata volume (or User CPUs) and duration times for the task attempts thatfall within the cell. The table depicts the TaskTracker nodes thatexecuted the tasks that are represented within the cell, and the numberof task attempts run on that node.

FIG. 31 depicts an example screenshot showing user environment 3100showing reports of resource consumption and usage statistics in thecomputing cluster. Reports of use by user and by service can begenerated and illustrated.

FIG. 32 depicts an example screenshot showing user environment 3200 forviewing health and performance data of an HDFS service.

FIG. 33 depicts an example screenshot showing user environment 3300 forviewing a snapshot of system status at the host machine level of thecomputing

Some pages, such as the services summary and service status pages, showstatus information from a single point in time (a snapshot of thestatus). By default, this status and health information is for thecurrent time. By moving the time marker to an earlier point on the timerange graph, the status as it was at the selected point in the past canbe shown.

In one embodiment, when displayed data is from a single point in time (asnapshot) the panel or column will display a small version of the timemarker icon in the panel. This indicates that the data corresponds tothe time at the location of the time marker on the time range selector.Under the activities tab with an individual activity selected, a zoom toduration button is available to allow users to zoom the time selectionto include just the time range that corresponds to the duration of theselected activity. FIG. 34 depicts an example screenshot showing userenvironment 3400 for viewing and diagnosing cluster workloads.

FIG. 35 depicts an example screenshot showing user environment 3500 forgathering, viewing, and searching logs.

The logs page presents log information for Hadoop services, which can befiltered by service, role, host, and/or search phrase as well log level(severity). The log search associated with a service can be within aselected time range. The search can be limited by role (only the rolesrelevant to this service instance will be available), by minimum loglevel, host, and/or keywords. From the logs list can provide a link to ahost status page, or to the full logs where a given log entry occurred.

The search results can be displayed in a list with the followingcolumns:

Host: The host where this log entry appeared. Clicking this link willretrieve the Host Status page

Log Level: The log level (severity) associated with this log entry.

Time: The date and time this log entry was created.

Source: The class that generated the message.

Message: The message portion of the log entry. Clicking a messageenables access to the Log Details page, which presents a display of thefull log, showing the selected message and the 100 messages before andafter it in the log.

These two charts show the distribution of log entries by log level, andthe distribution of log entries by host, for the subset of log entriesdisplayed on the current page.

FIG. 36 depicts an example screenshot showing user environment 3600 fortracking and viewing events across a computing cluster.

In general, the events can be searched within a selected timerange—which can be indicated on the tab itself. The search can be forevents of a specific type, for events that occurred on a specific host(for services—for a role, only the host for the role is searched), forevents whose description includes selected keywords, or a combination ofthose criteria. In addition, it can be specified that only events thatgenerated alerts should be included. In one embodiment, the list ofevents provides a link back to the service instance status page, therole instance status, or the host status page.

In one embodiment, the search criteria include all event types, allservices, and all hosts, with no keywords included. Modifying the searchcriteria can be optional In addition, it can be specified that onlyevents that generated alerts should be included.

The charts above the results list show the distribution of events by thetype of event, severity, and service. Note that these charts show thedistribution of events shown on the current page of the results list(where the number on the page is determined by the value in the Resultsper Page field). If there are multiple pages of results, these chartsare updated each time new sets of results are displayed. The chart canbe saved as a single image (a .PNG file) or a PDF file

FIG. 37 depicts an example screenshot showing user environment 3700 forrunning and viewing reports on system performance and usage.

The reports page enables users to create reports about the usage of HDFSin a computing cluster—data size and file count by user, group, ordirectory. It also generates reports on the MapReduce activity in acluster, by user. These reports can be used to view disk usage over aselected time range. The usage statistics can be reported per hour, day,week, month, or year. In one embodiment, for weekly or monthly reports,the date can indicate the date on which disk usage was measured. Thedirectories shown in the Historical Disk Usage by Directory reportinclude the HDFS directories that are set as watched directories.

FIG. 38-39 depicts example screenshots 3800 and 3900 showing timeinterval selectors 3804 for selecting a time frame within which to viewservice information. Feature 3804 can be used to switch back tomonitoring system status in current time or real time.

In one embodiment, the time selector appears as a bar when in the viewfor the services, activities, logs, and events tabs. In general, thehosts tab shows the current status, and the historical reports availableunder the reports tab also include time range selection mechanisms. Thebackground chart in the time Selector bar can show the percentage of CPUutilization on all machines in the cluster which can be updated atapproximately one-minute intervals, depending on the total visible timerange. This graph can be used to identify periods of activity that maybe of interest.

FIG. 40 depicts a table showing examples of different levels of healthmetrics and statuses.

The health check results are presented in the table, and some can alsobe charted. Other metrics are illustrated as charts over a time range.The summary results of health can be accessed under the Status tab,where various health results determine an overall health assessment ofthe service or role. In addition, the health of a variety of individualmetrics for HDFS, MapReduce and HBase service and role instances ismonitored. Such results can be accessed in the Health Tests panel underthe Status tab when an HDFS, MapReduce or HBase service or role instanceare selected.

The overall health of a role or service is a roll-up of the healthchecks. In general, if any health check is bad, the service's or role'shealth will be bad. If any health check is concerning (but none are bad)the role's or service's health will be concerning.

FIG. 41 depicts another screenshot showing the user environment 4100 formonitoring health and status information for MapReduce service runningon a computing cluster.

There are several types of health checks that are performed for an HDFS,HBase or MapReduce service or role instance including, for example:

Pass/fail checks, such as a service or role started as expected, aDataNode is connected to its NameNode, or a TaskTracker is (or is not)blacklisted. These checks result in the health of that metric beingeither good or bad.

Metric-type tests, such as the number of file descriptors in use, theamount of disk space used or free, how much time spent in garbagecollection, or how many pages were swapped to disk in the previous 15minutes. The results of these types of checks can be compared tothreshold values that determine whether everything is OK (e.g. plenty ofdisk space available), whether it is “Concerning” (disk space gettinglow), or is “bad” (a critically low amount of disk space).

In one embodiment, HDFS (NameNode) and HBase also run a health testknown as the “canary” test; it periodically does a set of simple create,write, read, and delete operations to determine the service is indeedfunctioning. In general, most health checks are enabled by default and(if appropriate) configured with reasonable thresholds. The thresholdvalues can be modified by editing the monitoring properties (e.g., underConfiguration tab for HDFS, MapReduce or HBase). In addition, individualor summary health checks can be enabled or disabled, and in some casesspecify what should be included in the calculation of overall health forthe service or role.

HDFS, MapReduce, and HBase services provide additional statistics aboutits operation and performance, for example, the HDFS summary can includeread and write latency statistics and disk space usage, the MapReduceSummary can include statistics on slot usage, jobs, and the HBaseSummary can include statistics about get and put operations and othersimilar metrics.

FIG. 42 depicts a table showing examples of different service or roleconfiguration statuses. The role summary provides basic informationabout the role instance, where it resides, and the health of its host.Each role types provide Role Summary and Processes panels, as well asthe Events and Logs tabs. Some role instances related to HDFS,MapReduce, and HBase also provide a Health Tests panel and associatedcharts.

FIG. 43-44 depicts example screenshots showing the user environment 4300and 4400 for accessing a history of commands 4402 issued for a service(e.g., HUE) in the computing cluster.

FIG. 45-49 depict example screenshots showing example user interfacesfor managing configuration changes and viewing configuration history.

FIG. 50A depicts an example screenshot showing the user environment 5000for viewing jobs and running job comparisons with similar jobs.

The system's activity monitoring capability monitors the jobs that arerunning on the cluster. Through this feature, operators can view whichusers are running jobs, both at the current time and through views ofhistorical activity, and it provides many statistics about theperformance of and resources used by those jobs. When the individualjobs are part of larger workflows (via Oozie, Hive, or Pig), these jobscan be aggregated into ‘activities’ that can be monitored as a whole aswell as by the component jobs. From the activities tab information aboutthe activities (jobs and tasks) that have run in the cluster during aselected time span can be viewed.

The list of activities provides specific metrics about the activitiesactivity that were submitted, were running, or finished within aselected time frame. Charts that show a variety of metrics of interest,either for the cluster as a whole or for individual jobs can bedepicted. Individual activities can be selected and drilled down to lookat the jobs and tasks spawned by the activity. For example, view thechildren of a Pig, Hive or Oozie activity—the MapReduce jobs it spawns,view the task attempts generated by a MapReduce job, view the activityor job statistics in a report format, compare the selected activity to aset of other similar activities, to determine if the selected activityshowed anomalous behavior, and/or display the distribution of taskattempts that made up a job, by amount of input or output data or CPUusage compared to task duration.

This can be used to determine if tasks running on a certain host areperforming slower than average. The compare tab can be used to view theperformance of the selected job compared with the performance of othersimilar jobs. In one embodiment, the system identifies jobs that aresimilar to each other (jobs that are basically running the same code—thesame Map and Reduce classes, for example). For example, the activitycomparison feature compares performance and resource statistics of theselected job to the mean value of those statistics across a set of themost recent similar jobs. The table can provide indicators of how theselected job deviates from the mean calculated for the sample set ofjobs, and provides the actual statistics for the selected job and theset of the similar jobs used to calculate the mean. FIG. 50B depicts atable showing the functions provided via the user environment of FIG.50A. FIG. 50C-D depict example legends for types of jobs and differentjob statuses shown in the user environment of FIG. 50A.

FIG. 51-52 depict example screenshots 5100 and 5200 depicting userinterfaces which show resource and service usage by user.

A tabular report can be queried or generated to view aggregate jobactivity per hour, day, week, month, or year. In the Report Periodfield, the user can select the period over which the metrics areaggregated. For example, it the user elects to aggregate by User,Hourly, the report will provide a row for each user for each hour.

For weekly reports, the date can indicate the year and week number (e.g.2011-01 through 2011-52). For monthly reports, the date typicallyindicates the year and month by number (2011-01 through 2011-12). Theactivity data in these reports comes from the activity monitor and caninclude the data currently in the Activity Monitor database.

FIG. 53-57 depict example screenshots showing user interfaces formanaging user accounts.

The manager accounts allow users to log into the console. In oneembodiment, the user accounts can either have administrator privilegesor no administrator privileges: For example, admin privileges can allowthe user to add, change, delete, and configure services or administeruser accounts and user accounts that without administrator privilegescan view services and monitoring information but cannot add services ortake any actions that affect the state of the cluster.

This list shown in the example of FIG. 53 shows the user accounts. Inaddition to the user's identifying information, the list can alsoinclude the following information:

The user's Primary Group, if one has been assigned; whether the accounthas been enabled: A check appears in the active column for an enabledaccount. The date and time of the user's last login into Hue.

Three example levels of user privileges include:

Superusers—have all permissions to perform any administrative function.A superuser can create more superusers and user accounts, and can alsochange any existing user account into a superuser. Superusers add thegroups, add users to each group, and add the group permissions thatspecify which applications group members are allowed to launch and thefeatures they can use. Superusers can modify MapReduce queue accesscontrol lists (ACLs). A superuser can also import users and groups froman LDAP server. In some instances, the first user who logs into afterits initial installation automatically becomes the superuser.

Users—have the permissions specified by the union of their groupmemberships to launch and use Hue applications. Users may also haveaccess privileges to Hadoop services. Imported users are those that havebeen imported from an LDAP server, such as Active Directory. There arerestrictions on the degree to which, a supervisor, for example, canmanage imported users.

Group administrators—have administration rights for selected groups ofwhich they are members. They can add and remove users, create subgroups,and set and remove permissions for the groups they administer, and anysubgroups of those groups. In other respects, they can behave likeregular users. The table shown in the example of FIG. 54 summarizes theauthorization manager permissions for superusers, group administrators,and users. The table of FIG. 56 describes the options in the add userdialog box shown in the example of FIG. 55. FIG. 58-59 depict examplescreenshots showing user interfaces for viewing applications recentlyaccessed by users.

FIG. 60-62 depict example screenshots showing user interfaces formanaging user groups.

Superusers and Group Administrators can typically add groups, delete thegroups they have created, configure group permissions, and assign usersto group memberships, as shown in th example of FIG. 60-FIG. 61. Ingeneral, a group administrator can perform these functions for thegroups that he administers, and their subgroups. A Superuser cantypically perform these functions for all groups. Users can add andremove users, and create subgroups for groups created manually inAuthorization Manager.

FIG. 63-64 depict example screenshots showing user interfaces formanaging permissions for applications by service or by user groups.

Permissions for applications can be granted to groups, with usersgaining permissions based on their group membership, for example. In oneembodiment, superusers and group administrators can assign or removepermissions from groups, including groups imported from LDAP.Permissions can be set by a group administrator for the groups sheadministers. In one embodiment, a superuser can set permissions for anygroup.

Permissions for Hadoop services, such as Fair Scheduler access, can beset for groups or for individual users. Group permissions can define theapplications that group members are allowed to launch, and the featuresthey can use. In general, subgroups inherit the permissions of theirparent group. A superuser or group administrator can turn off inheritedpermissions for a subgroup, thereby further restricting access forsubgroup members, and can re-enable those permissions (as long as theyremain enabled for the parent group). However, if permission is disabledfor the parent, it cannot be enabled for the subgroups of that parent.

Permissions can be assigned by service or by group. Assigningpermissions by group means that the assignment process starts with thegroup, and then application or service privileges to assign to thatgroup can be selected. Assigning permissions By Service means theassignment process starts with an application or service and aprivilege, and then the groups that should have access to that servicecan be selected.

In one embodiment, superuser or a group administrators can specify theusers and groups that have access privileges when access control isenabled. Service permissions can be granted both to groups and toindividual users. Granting permissions to a group automatically grantsthose permissions to all its members, and to all members of itssubgroups.

FIG. 65 depicts an example screenshot showing the user environment 6500for viewing recent access information for users.

For each user, the report shows their name and primary group membership,the last login date and time, the IP address of the client system fromwhich the user connected, and the date and time of the last launch ofthe Hue application

FIG. 66-68 depicts example screenshots showing user interfaces forimporting users and groups from an LDAP directory.

FIG. 69 depicts an example screenshot showing the user environment 6900for managing imported user groups. The Import LDAP Users command can beused to import all groups found in the LDAP directory, many of which maynot be relevant to the users. The groups that are not of interest fromthe Manage LDAP Groups page can be hidden.

FIG. 70 depicts an example screenshot 7000 showing the user environmentfor viewing LDAP status.

The sync timestamps show the dates and times of the most recent LDAPdirectory synchronization, and the most recent successful andunsuccessful syncs. If periodic LDAP synchronization is disabled, manualsynchronization can be used on demand. Sync with LDAP Now can be used toinitiate database synchronization. This causes an immediate sync withthe LDAP directory, regardless of whether periodic sync is enabled.

FIG. 71 shows a diagrammatic representation of a machine in the exampleform of a computer system within which a set of instructions, forcausing the machine to perform any one or more of the methodologiesdiscussed herein, may be executed.

In the example of FIG. 71, the computer system 7100 includes aprocessor, memory, non-volatile memory, and an interface device. Variouscommon components (e.g., cache memory) are omitted for illustrativesimplicity. The computer system 7100 is intended to illustrate ahardware device on which any of the components depicted in the exampleof FIG. 1 (and any other components described in this specification) canbe implemented. The computer system 7100 can be of any applicable knownor convenient type. The components of the computer system 7100 can becoupled together via a bus or through some other known or convenientdevice.

The processor may be, for example, a conventional microprocessor such asan Intel Pentium microprocessor or Motorola power PC microprocessor. Oneof skill in the relevant art will recognize that the terms“machine-readable (storage) medium” or “computer-readable (storage)medium” include any type of device that is accessible by the processor.

The memory is coupled to the processor by, for example, a bus. Thememory can include, by way of example but not limitation, random accessmemory (RAM), such as dynamic RAM (DRAM) and static RAM (SRAM). Thememory can be local, remote, or distributed.

The bus also couples the processor to the non-volatile memory and driveunit. The non-volatile memory is often a magnetic floppy or hard disk, amagnetic-optical disk, an optical disk, a read-only memory (ROM), suchas a CD-ROM, EPROM, or EEPROM, a magnetic or optical card, or anotherform of storage for large amounts of data. Some of this data is oftenwritten, by a direct memory access process, into memory during executionof software in the computer 7100. The non-volatile storage can be local,remote, or distributed. The non-volatile memory is optional becausesystems can be created with all applicable data available in memory. Atypical computer system will usually include at least a processor,memory, and a device (e.g., a bus) coupling the memory to the processor.

Software is typically stored in the non-volatile memory and/or the driveunit. Indeed, for large programs, it may not even be possible to storethe entire program in the memory. Nevertheless, it should be understoodthat for software to run, if necessary, it is moved to a computerreadable location appropriate for processing, and for illustrativepurposes, that location is referred to as the memory in this paper. Evenwhen software is moved to the memory for execution, the processor willtypically make use of hardware registers to store values associated withthe software, and local cache that, ideally, serves to speed upexecution. As used herein, a software program is assumed to be stored atany known or convenient location (from non-volatile storage to hardwareregisters) when the software program is referred to as “implemented in acomputer-readable medium.” A processor is considered to be “configuredto execute a program” when at least one value associated with theprogram is stored in a register readable by the processor.

The bus also couples the processor to the network interface device. Theinterface can include one or more of a modem or network interface. Itwill be appreciated that a modem or network interface can be consideredto be part of the computer system 1900. The interface can include ananalog modem, isdn modem, cable modem, token ring interface, satellitetransmission interface (e.g. “direct PC”), or other interfaces forcoupling a computer system to other computer systems. The interface caninclude one or more input and/or output devices. The I/O devices caninclude, by way of example but not limitation, a keyboard, a mouse orother pointing device, disk drives, printers, a scanner, and other inputand/or output devices, including a display device. The display devicecan include, by way of example but not limitation, a cathode ray tube(CRT), liquid crystal display (LCD), or some other applicable known orconvenient display device. For simplicity, it is assumed thatcontrollers of any devices not depicted in the example of FIG. 71 residein the interface.

In operation, the computer system 7100 can be controlled by operatingsystem software that includes a file management system, such as a diskoperating system. One example of operating system software withassociated file management system software is the family of operatingsystems known as Windows® from Microsoft Corporation of Redmond, Wash.,and their associated file management systems. Another example ofoperating system software with its associated file management systemsoftware is the Linux operating system and its associated filemanagement system. The file management system is typically stored in thenon-volatile memory and/or drive unit and causes the processor toexecute the various acts required by the operating system to input andoutput data and to store data in the memory, including storing files onthe non-volatile memory and/or drive unit.

Some portions of the detailed description may be presented in terms ofalgorithms and symbolic representations of operations on data bitswithin a computer memory. These algorithmic descriptions andrepresentations are the means used by those skilled in the dataprocessing arts to most effectively convey the substance of their workto others skilled in the art. An algorithm is here, and generally,conceived to be a self-consistent sequence of operations leading to adesired result. The operations are those requiring physicalmanipulations of physical quantities. Usually, though not necessarily,these quantities take the form of electrical or magnetic signals capableof being stored, transferred, combined, compared, and otherwisemanipulated. It has proven convenient at times, principally for reasonsof common usage, to refer to these signals as bits, values, elements,symbols, characters, terms, numbers, or the like.

It should be borne in mind, however, that all of these and similar termsare to be associated with the appropriate physical quantities and aremerely convenient labels applied to these quantities. Unlessspecifically stated otherwise as apparent from the following discussion,it is appreciated that throughout the description, discussions utilizingterms such as “processing” or “computing” or “calculating” or“determining” or “displaying” or the like, refer to the action andprocesses of a computer system, or similar electronic computing device,that manipulates and transforms data represented as physical(electronic) quantities within the computer system's registers andmemories into other data similarly represented as physical quantitieswithin the computer system memories or registers or other suchinformation storage, transmission or display devices.

The algorithms and displays presented herein are not inherently relatedto any particular computer or other apparatus. Various general purposesystems may be used with programs in accordance with the teachingsherein, or it may prove convenient to construct more specializedapparatus to perform the methods of some embodiments. The requiredstructure for a variety of these systems will appear from thedescription below. In addition, the techniques are not described withreference to any particular programming language, and variousembodiments may thus be implemented using a variety of programminglanguages.

In alternative embodiments, the machine operates as a standalone deviceor may be connected (e.g., networked) to other machines. In a networkeddeployment, the machine may operate in the capacity of a server or aclient machine in a client-server network environment, or as a peermachine in a peer-to-peer (or distributed) network environment.

The machine may be a server computer, a client computer, a personalcomputer (PC), a tablet PC, a laptop computer, a set-top box (STB), apersonal digital assistant (PDA), a cellular telephone, an iPhone, aBlackberry, a processor, a telephone, a web appliance, a network router,switch or bridge, or any machine capable of executing a set ofinstructions (sequential or otherwise) that specify actions to be takenby that machine.

While the machine-readable medium or machine-readable storage medium isshown in an exemplary embodiment to be a single medium, the term“machine-readable medium” and “machine-readable storage medium” shouldbe taken to include a single medium or multiple media (e.g., acentralized or distributed database, and/or associated caches andservers) that store the one or more sets of instructions. The term“machine-readable medium” and “machine-readable storage medium” shallalso be taken to include any medium that is capable of storing, encodingor carrying a set of instructions for execution by the machine and thatcause the machine to perform any one or more of the methodologies of thepresently disclosed technique and innovation.

In general, the routines executed to implement the embodiments of thedisclosure, may be implemented as part of an operating system or aspecific application, component, program, object, module or sequence ofinstructions referred to as “computer programs.” The computer programstypically comprise one or more instructions set at various times invarious memory and storage devices in a computer, and that, when readand executed by one or more processing units or processors in acomputer, cause the computer to perform operations to execute elementsinvolving the various aspects of the disclosure.

Moreover, while embodiments have been described in the context of fullyfunctioning computers and computer systems, those skilled in the artwill appreciate that the various embodiments are capable of beingdistributed as a program product in a variety of forms, and that thedisclosure applies equally regardless of the particular type of machineor computer-readable media used to actually effect the distribution.

Further examples of machine-readable storage media, machine-readablemedia, or computer-readable (storage) media include but are not limitedto recordable type media such as volatile and non-volatile memorydevices, floppy and other removable disks, hard disk drives, opticaldisks (e.g., Compact Disk Read-Only Memory (CD ROMS), Digital VersatileDisks, (DVDs), etc.), among others, and transmission type media such asdigital and analog communication links.

Unless the context clearly requires otherwise, throughout thedescription and the claims, the words “comprise,” “comprising,” and thelike are to be construed in an inclusive sense, as opposed to anexclusive or exhaustive sense; that is to say, in the sense of“including, but not limited to.” As used herein, the terms “connected,”“coupled,” or any variant thereof, means any connection or coupling,either direct or indirect, between two or more elements; the coupling ofconnection between the elements can be physical, logical, or acombination thereof. Additionally, the words “herein,” “above,” “below,”and words of similar import, when used in this application, shall referto this application as a whole and not to any particular portions ofthis application. Where the context permits, words in the above DetailedDescription using the singular or plural number may also include theplural or singular number respectively. The word “or,” in reference to alist of two or more items, covers all of the following interpretationsof the word: any of the items in the list, all of the items in the list,and any combination of the items in the list.

The above detailed description of embodiments of the disclosure is notintended to be exhaustive or to limit the teachings to the precise formdisclosed above. While specific embodiments of, and examples for, thedisclosure are described above for illustrative purposes, variousequivalent modifications are possible within the scope of thedisclosure, as those skilled in the relevant art will recognize. Forexample, while processes or blocks are presented in a given order,alternative embodiments may perform routines having steps, or employsystems having blocks, in a different order, and some processes orblocks may be deleted, moved, added, subdivided, combined, and/ormodified to provide alternative or subcombinations. Each of theseprocesses or blocks may be implemented in a variety of different ways.Also, while processes or blocks are at times shown as being performed inseries, these processes or blocks may instead be performed in parallel,or may be performed at different times. Further any specific numbersnoted herein are only examples: alternative implementations may employdiffering values or ranges.

The teachings of the disclosure provided herein can be applied to othersystems, not necessarily the system described above. The elements andacts of the various embodiments described above can be combined toprovide further embodiments.

Any patents and applications and other references noted above, includingany that may be listed in accompanying filing papers, are incorporatedherein by reference. Aspects of the disclosure can be modified, ifnecessary, to employ the systems, functions, and concepts of the variousreferences described above to provide yet further embodiments of thedisclosure.

These and other changes can be made to the disclosure in light of theabove Detailed Description. While the above description describescertain embodiments of the disclosure, and describes the best modecontemplated, no matter how detailed the above appears in text, theteachings can be practiced in many ways. Details of the system may varyconsiderably in its implementation details, while still beingencompassed by the subject matter disclosed herein. As noted above,particular terminology used when describing certain features or aspectsof the disclosure should not be taken to imply that the terminology isbeing redefined herein to be restricted to any specific characteristics,features, or aspects of the disclosure with which that terminology isassociated. In general, the terms used in the following claims shouldnot be construed to limit the disclosure to the specific embodimentsdisclosed in the specification, unless the above Detailed Descriptionsection explicitly defines such terms. Accordingly, the actual scope ofthe disclosure encompasses not only the disclosed embodiments, but alsoall equivalent ways of practicing or implementing the disclosure underthe claims.

While certain aspects of the disclosure are presented below in certainclaim forms, the inventors contemplate the various aspects of thedisclosure in any number of claim forms. For example, while only oneaspect of the disclosure is recited as a means-plus-function claim under35 U.S.C. §112, ¶6, other aspects may likewise be embodied as ameans-plus-function claim, or in other forms, such as being embodied ina computer-readable medium. (Any claims intended to be treated under 35U.S.C. §112, ¶6 will begin with the words “means for”.) Accordingly, theapplicant reserves the right to add additional claims after filing theapplication to pursue such additional claim forms for other aspects ofthe disclosure.

What is claimed is:
 1. A method of centralized configuration of adistributed computing cluster, the method, comprising: providing a userenvironment enabling a selection of a service to be run on hosts in thedistributed computing cluster; wherein, the user environment furtherenables configuration of the service or hosts in the distributedcomputer cluster; configuring each of the hosts in the distributedcomputing cluster to run the service based on a set of configurationsettings.
 2. The method of claim 1, further comprising, deploying agentsto the hosts in the distributed computing cluster to configure each ofthe hosts.
 3. The method of claim 1, further comprising, generatingrecommended configuration settings of the service or the hosts in thedistributed computing cluster to run the service; wherein, therecommended configuration settings include suggested ranges forparameters and invalid values for the parameters.
 4. The method of claim1, wherein, the user environment further enables, actions to beperformed on the set of configuration settings, the actions including,one or more of, reading, validating, updating, and deleting.
 5. Themethod of claim 1, wherein, the user environment further enablesaddition of services and reconfiguration of the service.
 6. The methodof claim 1, wherein, the user environment further enables assignment andre-assignment of roles to each of the hosts to indicate a function underthe service that each of the hosts is to perform.
 7. The method of claim1, wherein, the user environment further enables user configuration ofthe hosts with functions to perform under the service.
 8. The method ofclaim 1, wherein, the user environment further enables the selection ofthe service during an installation phase.
 9. The method of claim 1,wherein, the user environment further displays current or historicalhealth status of the hosts in the distributed computing cluster.
 10. Themethod of claim 1, wherein, the user environment further displayscurrent or historical disk usage, CPU, virtual memory consumption, orRAM usage of the hosts in the distributed computing cluster.
 11. Themethod of claim 1, wherein, the user environment further indicatescurrent or historical performance metrics or operational status of thehosts in the distributed computing cluster.
 12. The method of claim 1,wherein, the user environment further indicates, one or more of, currentor historical performance metrics of the service, a history of actionsperformed on the service, or a log of configuration changes of theservice.
 13. The method of claim 1, wherein, the user environmentfurther displays performance metrics of a job or comparison orperformance of similar jobs.
 14. The method of claim 1, wherein, theuser environment further indicates current user activities or historicaluser activities on the distributed computing cluster.
 15. The method ofclaim 1, wherein, the user environment further displays history ofactivity monitoring data and configuration changes of the hosts in thedistributed computing cluster.
 16. The method of claim 1, wherein, theuser environment further provides access to log entries associated withthe service or events in the distributed computing cluster.
 17. Themethod of claim 16, wherein, the events include, one or more of, arecord of change of state of health of the server, a message has beenlogged, a service has been added or reconfigured, a new job has beensetup, an error, a change in operational or on/off state of a givenhost.
 18. The method of claim 16, wherein, the events include, one ormore of, a health check event, a log message event, an audit event, andan activity event.
 19. The method of claim 16, wherein, the events aresearchable via the user environment.
 20. The method of claim 17,wherein, the user environment further enables search or filtering of thelog entries by one or more of, time range, service, host, keyword, anduser.
 21. The method of claim 17, wherein, the user environment furtherdepicts alerts triggered by certain events or actions in the distributedcomputing cluster.
 22. The method of claim 1, wherein, the userenvironment further displays operational reports.
 23. The method ofclaim 20, wherein, the operational reports include, one or more of, diskuse by user, user group, or directory, cluster job activity by user,group or job ID.
 24. The method of claim 1, wherein, the userenvironment further enables configuration of delivery of alerts.
 25. Themethod of claim 1, wherein, the service includes one or more Hadoopservices.
 26. The method of claim 3, wherein, the Hadoop servicesinclude one or more of, MapReduce, HDFS, Hue, ZooKeeper and Oozie. 27.The method of claim 1, wherein, the user environment is accessed via aweb browser.
 28. A system to manage and configure a computing cluster,the system, comprising: a server, which when in operation, tracks orupdates a data model with a catalog of hosts in the computing cluster;provides a console for management and configuration of services to bedeployed in the computing cluster; configures agents to be deployed tothe hosts in the computing cluster based on configuration settings;monitors health and performance metrics of the hosts and the services;depicts in the console, the health and the performance metrics of thehosts and the services.
 29. The system of claim 28, wherein, the serverfurther maintains a history of the health and the performance metrics.30. The system of claim 28, wherein, the server deploys the agents toeach of the hosts and communicates with the agents to send theconfiguration settings to configure each of the hosts in the computingcluster.
 31. The system of claim 30, wherein, the agents start and stopdaemons on each of the hosts to run the services.
 32. The system ofclaim 30, wherein, the agents create directories, processes, and fileson hosts in a user-specific manner.
 33. The system of claim 30, wherein,the agents aggregate statistics regarding each of the hosts.
 34. Thesystem of claim 31, wherein, the server performs health calculations ofthe hosts based on the statistics collected by the agents.
 35. Thesystem of claim 28, wherein, the services include Hadoop services. 36.The system of claim 28, wherein, the data model is stored in arepository coupled to the server; wherein, the data model furtherspecifies, one or more of, services, roles, and configurations assignedto each of the hosts.
 37. The system of claim 30, wherein, the datamodel further stores configuration or monitoring information regardingthe daemons on each of the hosts.
 38. A method to manage and configure acomputing cluster with a Hadoop stack, the method, comprising:configuring hosts in the computing cluster based on configurationsettings and Hadoop services to be run in the computing cluster;monitors health and performance metrics of the hosts and the Hadoopservices; computing, the health and the performance metrics of the hostsand the Hadoop services.
 39. The method of claim 38, wherein, theconfiguration settings are specified via a console accessible via a webinterface.
 40. The method of claim 38, further comprising, depicting inthe console, the health and the performance metrics of the hosts and theservices.
 41. The method of claim 38, wherein, the console includes atime selector feature allowing selection of a time range within which toview the services, activities, logs, or events.
 42. The method of claim38, further comprising, providing, via the console, enablement of aselection of a service during installation to be run on hosts in thedistributed computing cluster.
 43. The method of claim 38, furthercomprising, providing recommended configuration settings of the Hadoopservice or the hosts in the computing cluster to run the service;wherein, the recommended configuration settings include suggested rangesfor parameters and invalid values for the parameters.
 44. The method ofclaim 38, wherein, the health and the performance metrics includescurrent information regarding the computing cluster in real time or nearreal time; wherein, the current information is accessible via a consoleaccessible through a web interface which is also used to configure thehosts.
 45. The method of claim 38, wherein, the health and theperformance metrics includes historical information regarding thecomputing cluster; wherein, the current information is accessible via aconsole accessible through a web interface which is also used toconfigure the hosts.
 46. The method of claim 38, further comprising:generating an alert responsive to detecting an event in the computingcluster meeting a criteria; wherein, the alert is deliverable via email.47. A computing cluster configured for distributed computing, thecomputing cluster, comprising: multiple machines coupled in a network;wherein, each machine is able to run one or more services fordistributed computing in the computing cluster; wherein, theconfiguration of the one or more services on each machine in thecomputing cluster is configurable via a centralized management console;wherein, health or performance information regarding each machine isalso monitored and accessed via the centralized management console. 48.The computing cluster of claim 47, wherein, the centralized managementconsole is accessible via a web browser.
 49. The computing cluster ofclaim 47, wherein, each of the multiple machines are configured by aserver remotely coupled to the computing cluster which hosts thecentralized management console.
 50. The computing cluster of claim 49,wherein, logs of the services and events are accessible and searchablevia the centralized management console.
 51. The computing cluster ofclaim 49, wherein, the server computes statistics from the health andperformance information and stores a history of the health andperformance information for access and search via the centralizedmanagement console.
 52. The computing cluster of claim 47, wherein:roles are assigned to each of the machines to indicate a function underthe service that each of the hosts is to perform.
 53. The computingcluster of claim 48, wherein, the computing cluster is configured to runa distributed file system service; wherein, via the centralizedmanagement console, one of the machines is configured to run asNameNode, one of the machines is selected to run as a SecondaryNameNode, another one of the machines is selected to run as a Balance,and one or more of the remaining machines as Datanodes.
 54. Thecomputing cluster of claim 49, wherein, the distributed file systemservice is a Hadoop distributed file system service (HDFS).
 55. Thecomputing cluster of claim 48, wherein, the computing cluster isconfigured to run a parallel processing service; wherein, via thecentralized management console, one of the machines is configured todelegate tasks to select machines in the computing cluster, andconfigure the select machines to accept the tasks.
 56. The computingcluster of claim 51, wherein, the parallel processing service includes aMapReduce service, and the one of the machines is configured to run as aJobTracker role instance and the select machines are configured to runas TaskTracker role instances.
 57. The computing cluster of claim 47,wherein, the service includes one or more Hadoop services.
 58. Thecomputing cluster of claim 47, wherein, the Hadoop services include oneor more of, Hbase, Hue, ZooKeeper and Oozie.
 59. The computing clusterof claim 47, wherein, the Hadoop services include one or more of, HadoopCommon, Avro, Cassandra, Chukwa, Hive, Mahout, and Pig.